Machine Learning Engineer

Zhongwei Zhou

Sr. Machine Learning Engineer

+1-847-***-**** *********@*****.*** Coral Springs, FL Professional Summary

● Led 15+ machine learning projects from concept to deployment, delivering AI-driven solutions that addressed complex business challenges across multiple domains.

● Developed and deployed 50+ machine learning models utilizing frameworks like TensorFlow, PyTorch, and Keras, with a focus on NLP, computer vision, and generative tasks.

● Built and fine-tuned Generative AI models (e.g., GANs, VAEs, Transformers) to automate content generation, natural language understanding, and data augmentation, leveraging architectures such as GPT-4 and BERT.

● Implemented large language models (LLMs) for various NLP tasks, including text summarization, sentiment analysis, and question-answering systems, using transformer models like T5, RoBERTa, and OpenAI GPT APIs.

● Accelerated model deployment pipelines by automating workflows using MLOps tools such as MLflow, Kubeflow, and AWS Sagemaker, ensuring continuous integration and delivery.

● Applied Zero-Shot and Few-Shot Learning techniques to enhance model performance with minimal training data, especially in low-resource environments.

● Integrated GPT-based APIs and LLM-powered chatbots into production environments, automating customer interactions and enabling real-time, scalable AI solutions.

● Utilized Transfer Learning with pre-trained models to reduce training times and achieve high performance in specialized tasks, including image classification and text generation.

● Optimized hyperparameter tuning and model training using AutoML tools like Google AutoML and Bayesian Optimization, reducing manual tuning effort and improving model efficiency.

● Enhanced model transparency and interpretability by applying SHAP and LIME for AI explainability, ensuring models were aligned with ethical and responsible AI practices.

● Deployed and managed AI models on cloud platforms such as AWS, GCP, and Azure, focusing on scalable solutions and cost-efficient resource utilization.

● Worked on multimodal AI models, combining text, images, and audio data to create integrated solutions for recommendation systems, content classification, and more.

● Delivered real-time AI services through API-based AI solutions, enabling businesses to leverage machine learning models for on-demand predictions and automated decision-making. Technical Skills

Programming Languages: Python, R, Scala, Julia, C++, Java, SQL Machine Learning Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, LightGBM, CatBoost, Hugging Face Transformers, H2O.ai

Deep Learning: CNN, RNN, LSTM, GANs, Transformers, BERT, GPT-4, OpenAI API Natural Language Processing (NLP): SpaCy, NLTK, Hugging Face, BERT, GPT, T5, RoBERTa, Word2Vec, GloVe, FastText

Data Preprocessing: Pandas, NumPy, Dask, FeatureTools, OpenCV Data Visualization: Matplotlib, Seaborn, Plotly, Altair, ggplot, Bokeh Model Deployment: Docker, Kubernetes, Flask, FastAPI, TensorFlow Serving, TorchServe, AWS Sagemaker, GCP AI Platform

MLOps Tools: MLflow, Kubeflow, Airflow, DVC, Metaflow, SageMaker, Google AutoML AutoML: Google AutoML, H2O.ai, TPOT, AutoKeras

Big Data: Apache Spark, Hadoop, Databricks, Dask

Databases: SQL, PostgreSQL, MongoDB, Cassandra, DynamoDB, HDFS, Apache Hive, BigQuery Hyperparameter Tuning: GridSearchCV, RandomSearchCV, Optuna, Hyperopt, Bayesian Optimization Model Explainability: SHAP, LIME, InterpretML, Captum Cloud Technologies: AWS (SageMaker, EC2, S3), GCP (AI Platform, BigQuery), Azure (ML Studio) Version Control: Git, GitHub, Bitbucket

Operating Systems: Windows, Linux (Ubuntu, CentOS), macOS Development Tools/IDEs: Jupyter Notebook, PyCharm, VS Code, Google Colab, RStudio API Development: Flask, FastAPI, Django, REST API

Testing & Evaluation: JUnit, PyTest, Cross-Validation, A/B Testing, ROC Curve, Precision/Recall, Confusion Matrix, F1 Score

Professional Experience

Brainvire Infotech Lead ML Engineer Aug 2021 - Aug 2024

● Led the development and deployment of a machine learning model that improved customer retention by 30%, utilizing predictive analytics to enhance user engagement strategies.

● Directed a team of 5 ML engineers, fostering collaboration through Agile methodologies, resulting in a 25% increase in project delivery speed.

● Designed and implemented a natural language processing (NLP) model that achieved 90% accuracy in sentiment analysis, aiding in real-time customer feedback assessment.

● Developed a recommendation system using collaborative filtering techniques that boosted product sales by 15% through personalized user experiences.

● Engineered data pipelines to preprocess and analyze large datasets, reducing data preparation time by 40% and ensuring high-quality inputs for model training.

● Integrated TensorFlow and PyTorch frameworks for developing deep learning models, optimizing performance through advanced hyperparameter tuning techniques.

● Deployed machine learning models on AWS using SageMaker, ensuring scalable and cost-effective solutions for processing over 1 million transactions per day.

● Implemented model monitoring and retraining processes, achieving an overall model performance improvement of 20% within the first six months post-deployment.

● Authored comprehensive documentation and conducted knowledge-sharing sessions, enhancing team competency in ML best practices and techniques.

Tools: TensorFlow, PyTorch, AWS SageMaker, Python, SQL, Pandas, NumPy, scikit-learn, Docker, Git, Jupyter Notebook. Unique Software Development Sr. ML Engineer Feb 2017 - Jul 2021

● Developed and deployed an end-to-end machine learning pipeline that processed real-time data from IoT devices, improving predictive maintenance accuracy by 25%.

● Collaborated in an Agile environment, participating in bi-weekly sprints and daily stand-ups, resulting in a 20% increase in team productivity and delivery timelines.

● Engineered a data visualization dashboard using Python and Plotly, allowing stakeholders to interact with ML model outputs, enhancing decision-making speed by 35%.

● Improved data ingestion processes by implementing Apache Kafka for real-time analytics, reducing data lag time from hours to minutes.

● Designed and implemented RESTful APIs for seamless integration of machine learning models with front-end applications, ensuring efficient data flow and response times under 200ms.

● Spearheaded migration of legacy systems to cloud-based architecture on AWS, resulting in a 40% reduction in operational costs.

● Enhanced team capabilities in machine learning through mentorship and training sessions, resulting in 3 successful ML project launches within 12 months.

● Conducted performance tuning and optimization of machine learning algorithms, resulting in a 15% reduction in execution time for key processes.

Tools: Python, Apache Kafka, AWS (EC2, S3), Plotly, Docker, Flask, Git, scikit-learn, Jupyter Notebook. GiniLytics IT Solutions ML Engineer Jun 2013 - Jan 2017

● Developed machine learning algorithms for processing and analyzing streaming healthcare data, leading to a 20% reduction in patient care response times.

● Participated in the design and implementation of a robust data warehouse solution using GCP, enabling efficient data retrieval and analytics, enhancing data accessibility by 30%.

● Collaborated on a team project to build a recommendation engine, achieving an 85% accuracy rate in predicting user preferences based on historical data.

● Utilized Docker for containerization of ML applications, ensuring consistent deployment across various environments and reducing deployment time by 50%.

● Conducted regular code reviews and implemented testing protocols that increased the overall code quality and reduced bug rates by 15%.

● Engaged in cross-functional collaboration with data scientists and product managers to identify opportunities for machine learning integration, contributing to a 25% increase in project scope and impact. Tools: GCP (Google Cloud Storage, BigQuery), Docker, Python, SQL, R, Apache Spark, TensorFlow, scikit-learn, Git, Jupyter Notebook.

Include Software Jr. Software Engineer Aug 2012 - May 2013

● Assisted in the development of a machine learning-based user authentication system that reduced unauthorized access attempts by 50%.

● Contributed to the creation of web-based applications using Python and Flask, implementing RESTful services that facilitated seamless integration with machine learning models.

● Collaborated with senior engineers in the implementation of data preprocessing and feature extraction techniques, increasing the efficiency of subsequent ML models by 30%.

● Engaged in Agile methodologies, participating in sprint planning and retrospectives, which contributed to improved team dynamics and project timelines.

● Developed initial prototypes for various machine learning applications using scikit-learn, demonstrating the feasibility of solutions that later led to full-scale projects.

● Supported QA testing efforts, including writing unit tests and troubleshooting issues, which contributed to a 15% increase in overall application stability.

● Participated in daily stand-ups and code reviews, enhancing team collaboration and ensuring adherence to coding standards.

Tools: Python, Flask, scikit-learn, SQL, RESTful APIs, JUnit, Git, Docker, Jupyter Notebook. Education

Florida Atlantic University Bachelor’s Degree in Computer Science Sep 2008 - Apr 2012

● Completed coursework in data structures, algorithms, and software engineering principles, laying a strong foundation for advanced machine learning applications.

● Engaged in hands-on projects, including developing a recommendation system using collaborative filtering, which enhanced skills in data analysis and Python programming.

● Participated in group projects that emphasized Agile methodologies, collaborating with peers to deliver functional software solutions within tight deadlines.

● Conducted research on emerging technologies in artificial intelligence, presenting findings at the university’s annual tech symposium, fostering a passion for continuous learning in the ML field.

● Served as a teaching assistant for introductory programming courses, enhancing communication skills and solidifying understanding of core concepts in computer science.

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